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.0em plus.0ex minus.2ex-.5emZero-Shot/Open-vocabulary object detection. It is costly and labor-intensive to scale up data collection and annotation for large vocabulary detection. Zero-shot detection aims to alleviate the challenge by learning to detect novel categories not present in the training data. Many methods address this by aligning the image region features to category word embeddings [1]}, [2]}, [3]}, [4]}, or synthesizing visual features with a generative model [5]}, [6]}. Recently, Zareian et al. proposes the open-vocabulary detection (OVD) benchmark with a view to bridge the performance gap between ZSD and supervised learning [7]}. The model was first pretrained on image-caption data to recognize novel objects, and then finetuned for zero-shot detection [7]}.
